Machine Learning Models for Phase Prediction in Polymer-Induced Self Assembly

Interpretable machine learning models for predicting the morphological outcomes of polymerization-induced self-assembly experiments inlcuding code for data handling, model evaluation, and phase diagram calculation.

Publication

This is the code and data repository encompanying the article:

Lu, Yiwen, Dilek Yalcin, Paul J. Pigram, Lewis D. Blackman, and Mario Boley. "Interpretable machine learning models for phase prediction in polymerization-induced self-assembly." Journal of Chemical Information and Modeling 63, no. 11 (2023): 3288-3306.

If you use any part of this repository in your work, we kindly ask you to cite our work as:

@article{lu2023interpretable,
  title={Interpretable machine learning models for phase prediction in polymerization-induced self-assembly},
  author={Lu, Yiwen and Yalcin, Dilek and Pigram, Paul J and Blackman, Lewis D and Boley, Mario},
  journal={Journal of Chemical Information and Modeling},
  volume={63},
  number={11},
  pages={3288--3306},
  year={2023},
  publisher={ACS Publications}
}

Setup

To replicate the experiments, you have to have Python of version at 3.9.11 installed on your machine. In addition you have to install dependencies via:

pip3 install -r requirements.txt